EEG assessment of brain dysfunction for patients with chronic primary pain and depression under auditory oddball task
Yunzhe Li1, Banghua Yang1,2, Zuowei Wang3
1School of Medicine, School of Mechatronic Engineering and Automation, Research Center of Brain Computer Engineering, Shanghai University, Shanghai, China.
Insights
Chronic primary pain (CPP) patients exhibit distinct brain network differences compared to depression patients. This study reveals enhanced frontoparietal network connectivity in CPP, offering a new objective assessment method.
Area of Science:
- Neuroscience
- Medical Informatics
- Psychiatry
Background:
- The International Classification of Diseases 11th Revision introduced "chronic primary pain" (CPP), a condition marked by significant disability and distress.
- While CPP is linked to depression, its underlying neural mechanisms remain poorly understood.
- Objective assessment methods for CPP are lacking.
Purpose of the Study:
- To investigate the neural characteristics of chronic primary pain (CPP) using electroencephalography (EEG).
- To differentiate brain network connectivity patterns between CPP patients, depression patients, and healthy controls.
- To develop a deep learning-based classification model for objective CPP assessment.
Main Methods:
- Collected EEG data from 67 participants (23 healthy, 22 depression, 22 CPP) during an auditory oddball paradigm.
- Analyzed brain network connectivity matrices and graph theory indicators across different frequency bands.
- Employed deep learning (Convolutional Neural Network - CNN) to classify EEG and Phase Lag Index (PLI) matrices.
Main Results:
- Significant differences in brain network connectivity were observed between CPP and depression patients.
- CPP patients showed significantly enhanced connectivity within the frontoparietal network in the Theta band.
- The CNN model achieved higher accuracy classifying EEG (85.01% in Gamma band) than PLI (79.64% in Theta band).
Conclusions:
- The findings suggest hyperexcitability in attentional control networks in chronic primary pain (CPP) patients.
- This study provides a novel, data-driven approach for the objective assessment of chronic primary pain.
- Distinct neural signatures differentiate CPP from depressive disorders, aiding in diagnosis and treatment.
Abstract:
In 2019, the International Classification of Diseases 11th Revision International Classification of Diseases (ICD-11) put forward a new concept of "chronic primary pain" (CPP), a kind of chronic pain characterized by severe functional disability and emotional distress, which is a medical problem that deserves great attention. Although CPP is closely related to depressive disorder, its potential neural characteristics are still unclear. This paper collected EEG data from 67 subjects (23 healthy subjects, 22 patients with depression, and 22 patients with CPP) under the auditory oddball paradigm, systematically analyzed the brain network connection matrix and graph theory characteristic indicators, and classified the EEG and PLI matrices of three groups of people by frequency band based on deep learning. The results showed significant differences in brain network connectivity between CPP patients and depressive patients. Specifically, the connectivity within the frontoparietal network of the Theta band in CPP patients is significantly enhanced. The CNN classification model of EEG is better than that of PLI, with the highest accuracy of 85.01% in Gamma band in former and 79.64% in Theta band in later. We propose hyperexcitability in attentional control in CPP patients and provide a novel method for objective assessment of chronic primary pain.
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